Visualizing Key Biodiversity Areas¶
Do we want to change this title??^^^
A Study Project in Collaboration with NatureServe¶
- Christy Sandberg, data processing and analysis
- Eric Nutt, scientific communication
- Lana Kurakina, GIS and spatial data processing support
- Elsa Culler, mentor
- Pat Comer, project partner
Biodiversity Unprotected is Biodiversity Lost¶
This is a critical time for conservation, as many ecosystems across the globe are at risk of being lost due to deforestation, construction, large-scale agriculture, human encroachment, climate change etc. There are multiple agencies and organizations working on both a regional and global level to fast-track conservation efforts.
Scientists bring together collected data from a variety of biological and ecological sources and execute a workflow to define areas that successfully meet the required criteria. These data sources can include:
- species populations,
- vegetative land cover,
- nesting sites,
- seasonal feeding patterns
- or even the location of historic cultural heritage sites.
In 2021, U.S. President Joe Biden launched the ‘America the Beautiful’ initiative, with the stated goal of conserving at least 30 percent of U.S. lands and waters by 2030. Globally, the International Union for Conservation of Nature (IUCN) has been working since 2004 on a standard for identifying Key Biodiversity Areas (KBAs), which are defined as areas essential to supporting the persistence of global biodiversity at either a species or ecosystem level. Once KBAs are established and the information is shared with stakeholders, that area becomes prioritized for conservation planning.
 |  |
Understanding the Problem¶
The IUCN is aware their KBA standard will evolve over time, and actively seeks input from users who work with the current criteria and may have suggestions for improvement. Our project sponsor Pat Comer is a member of the IUCN Commission on Ecosystem Management, and is interested in whether the current thresholds adequately allow for KBAs to be defined for all ecosystems.
Currently, the standard is set so that a KBA can be identified at a site that comprises 10% of the global extent of an ecosystem defined as ‘Vulnerable’ by the IUCN Red List of Ecosystems (RLE), or at 5% when the ecosystem is at the higher risk ‘Critical’ or ‘Endangered’ level.
Working Towards a Solution¶
Move below "Visualizing Key Biodiversity Areas"^^^???
Our project creates a toolkit for spatial analysis to help identify KBAs, across the continental United States. Our goal is to utilize computer coding to produce an automatic workflow that can analyze spatial distributions of any terrestrial ecosystem type and determine where it could meet the KBA threshold criteria, based on the relative proportion of its range wide extent. Our toolkit will also help determine if these current thresholds may be set too high, especially for ecosystems with linear or fragmented spatial characteristics. In these instances, it can be difficult to locate independent areas large enough to qualify for conservation using the current standard, or that are in close enough proximity to other areas to allow them to be managed together as a single conservation unit.
We aim to determine whether lower thresholds would be more successful in identifying KBAs for ecosystems with such spatial characteristics. If so, that may be a useful revision for the IUCN to make to their standard, as ultimately the goal is to identify KBAs for all threatened ecosystems.
Results of the analysis are shared with key stakeholders and decision makers, enabling them to efficiently direct limited resources towards solutions with the lowest cost and greatest chance for long term ecological success.
Area of Interest¶
Our team examined nine different terrestrial ecosystems across the continental United States. They are presented below as a shapefile generated from open source GIS software and clearly distrubute a diverse set of spatial characteristics. The individual ecosystems were extracted from a national raster dataset (Landfire, 2016).
Visualizing Key Biodiversity Areas¶
Our methodology for identifying and visualizing KBAs relies primarily on a software called Marxan. Simply put, Marxan analyzes the number of raster cells in each hexagon overlaying the ecosystem, and from that analysis determines what areas of the ecosystem are closer together, and which areas are further apart. The more bunched up the ecosystem in a given contiguous area, the more likely it is to meet the KBA criteria. To visualize this process, we used Marxan to generate a heat map of each ecosystem. Red cells on the map depict areas with little to no ecosystem present. As we move along the gradient, from yellow to green, more and more of that particular ecosystem is present within a single hex cell. Green on the map indicates areas where we are likely to meet the given KBA threshold.
Steps for the Future¶
Ultimately, we plan to share this workflow with scientists around the globe, so they can identify and protect their own KBAs to include threatened ecosystems and species. Therefore, we hope to make the current KBA identification process sensitive to a greater variety of ecosystem types, allowing it to be scaled up globally. KBA Identification is one key step in protecting these threatened ecosystems. If these areas are not identified, they are at risk of being destroyed, along with the biodiversity they support.
While our heat-maps provide a good indication for what areas of each ecosystem meets the threshold to be considered a KBA, we would like to enhance our workflow to generate a clear and precise answer for which areas should be protected. We are currently waiting for an update on the functionality of the Marxan software to allow to perform the final analytical step that will produce the discrete statistical data we are looking for.
Find Out More¶
To find out more about our project, please visit our project repository on GitHub by following the link below. https://github.com/csandberg303/kba-threshold-sensitivity-analysis Here you can find more information about the project, our python code workflows used to generate our findings, and other project resources.
Export Notebook as HTML File¶
Other Work Done in the Area¶
This is a critical time for conservation, as many ecosystems across the globe are at risk of being lost due to deforestation, construction, large-scale agriculture, human encroachment, climate change etc. There are multiple agencies and organizations working on both a regional and global level to fast-track conservation efforts.
In 2021, U.S. President Biden launched the ‘America the Beautiful’ initiative, with the stated goal of conserving at least 30 percent of U.S. lands and waters by 2030. Globally, the International Union for Conservation of Nature (IUCN) has been working since 2004 on a standard for identifying Key Biodiversity Areas (KBAs), which are defined as areas essential to supporting the persistence of global biodiversity at either a species or ecosystem level. Once KBAs are established and the information is shared with stakeholders, that area becomes prioritized for conservation planning.
The IUCN is aware their KBA standard will evolve over time, and actively seeks input from users who work with the current criteria and may have suggestions for improvement. Our project sponsor Pat Comer is a member of the IUCN Commission on Ecosystem Management, and is interested in whether the current thresholds adequately allow for KBAs to be defined for all ecosystems.
Currently, the standard is set so that a KBA can be identified at a site that comprises 10% of the global extent of an ecosystem defined as ‘Vulnerable’ by the IUCN Red List of Ecosystems (RLE), or at 5% when the ecosystem is at the higher risk ‘Critical’ or ‘Endangered’ level.
We would like to investigate if these current thresholds may be set too high, especially for ecosystems with linear or fragmented spatial characteristics. In these instances, it can be difficult to locate independent areas large enough to qualify for conservation using the current standard, or that are in close enough proximity to other areas to allow them to be managed together as a single conservation unit.
The question we are researching is whether lower thresholds would be more successful in identifying KBAs for ecosystems with such spatial characteristics. If so, that may be a useful revision for the IUCN to make to their standard, as ultimately the goal is to identify KBAs for all threatened ecosystems.
Methods¶
Data Sources¶
- LANDFIRE, 2016, Existing Vegetation Type Layer, LANDFIRE 2.0.0, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture. Accessed 28 October 2021 at http://www.landfire/viewer.
- Nested Hexagon Framework (NHF), developed by Mike Houts at the University of Kansas, provided by Pat Comer.
Software, Libraries, Packages¶
- Apropos Information Systems Inc. ArcMarxan Toolbox, Version 2.0.2; Available at https://aproposinfosystems.com/
- QMarxan Toolbox, Version 2.0.1
- Ball, I.R., H.P. Possingham, and M. Watts. 2009. Marxan and relatives: Software for spatial conservation prioritisation. Chapter 14: Pages 185-195 in Spatial conservation prioritisation: Quantitative methods and computational tools. Eds Moilanen, A., K.A. Wilson, and H.P. Possingham. Oxford University Press, Oxford, UK; Available at https://marxansolutions.org/software/
- QGIS Association. QGIS version 3.22.5-Białowieża, 2022. QGIS Geographic Information System. Available at http://www.qgis.org
- CLUZ, CLUZ plugin for QGIS v3 CLUZ. Conservation Land-Use Zoning software developed by Bob Smith from the Durrell Institute of Conservation and Ecology (DICE), and funded by the UK Government's Darwin Initiative. Available at http://anotherbobsmith.wordpress.com/software/cluz/
- J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.
- Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585:357–62.
- Kelsey Jordahl, et al. Geopandas Version v0.8.1. Zenodo. http://doi.org/10.5281/zenodo.3946761
- Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5(1), p.10. DOI: https://doi.org/10.5334/jors.148
- Rioxarray. Geospatial xarray extension pwered by rasterio. Avaialable at https://corteva.github.io/rioxarray/stable/
- EarthPy. A Python Package for Earth Data. Avaialable at https://earthpy.readthedocs.io/en/latest/index.html
Workflow Steps Completed¶
- Extract raster files featuring individual ecosystems from a national raster dataset (Landfire, 2016).
- Use NHF to prepare planning unit shapefiles.
- Use ArcMarxan/Qmarxan Toolbox to prepare input files for Marxan.
- Perform trial run of Marxan 4.06 using default input parameters.
- Repeat multiple runs of Marxan adjusting input parameters, such as conservation target.
- Visualize results.
Study Area¶
Currently our study area includes continental US. For the trial run we used nine ecosystems with diverse spatial characteristics:
- Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland
- Central Tallgrass Prairie
- Columbia Basin Palouse Prairie
- Crowley's Ridge Mesic Loess Slope Forest
- East Gulf Coastal Plain Northern Loess Bluff Forest
- Northern Atlantic Coastal Plain Tidal Salt Marsh
- South Florida Cypress Dome
- Southwest Florida Dune and Coastal Grassland
- Western Great Plains Foothill and Piedmont Grassland
Example of the Workflow Trial Run¶
Raster Featuring Individual Ecosystem¶
Planning Unit Shapefile¶
Use Planning Unit Shapefile and Raster to Run Zonal Stats Analysis¶
Visualize Results of Zonal Stats Analysis¶
Conclusions¶
We are aiming to conduct a sensitivity analysis of the current thresholds set by the IUCN for KBA identification. This will allow us to see if a lower threshold will be more effective in identifying areas to prioritize for conservation.
If we find lower thresholds are helpful in identifying KBAs for ecosystems with linear or fragmented spatial characteristics, this could be presented to the IUCN for inclusion in a future revision to the KBA standard which would lead to our findings being used globally.
Systematic conservation planning relies on a complex annealing algorithm function that accounts for a number of variables. This algorithm can be applied to a variety of conditions, depending on the input factors. The question of how to determine which input factors return the best outcomes can be capably answered using an iterative Python workflow to help manage the complexities and analyze the outcomes. An additional benefit of our Python workflow is that it will enable interested parties to review our results in an objective, repeatable and transparent way, or even use their own source data for a similar sensitivity analysis.
Questions to Explore¶
We are left with several questions at the end of this initial project phase:
Most obviously, how can we improve the workflow? Any open-source scientific workflow has room for improvement.
Can the entire workflow be completed in Python, or is a GIS software necessary? If it can, how? What tools are necessary?
How will the spatial characteristics of the ecosystems be analyzed affect their calculated KBA status?
Moving Forward¶
- Investigate API capabilities at http://www.landfire/viewer
- Test Cluz (QGIS plugin) capabilities to prepare Marxan Input files and visualize results.
- Try executing Marxan runs using Python/Jupyter notebook.
- Develop a reproducable workflow allowing to visualize results of Marxan runs.
- Set a publicly accessible data storage.